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\documentclass[openright,oneside]{ctexbook}	% 加[draft]选项可不显示图片本体，加快编译，全文完成后再删除
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\pagenumbering{arabic} % 阿拉伯数字页码
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\addcontentsline{toc}{chapter}{参考文献}
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%%完成后再改成thebib...
\begin{thebibliography}{99}
\bibitem{ck1} 李嘉璇，TensorFlow技术解析与实战[M],北京:人民邮电出版社,2017
\bibitem{ck2} 阿布, 胥嘉幸,机器学习之路:Caffe、Keras、scikit-learn实战[M],北京:电子工业出版社,2017
\bibitem{ck3} (美) 伊恩·古德费洛, (加) 约书亚·本吉奥, 亚伦·库维尔, 深度学习[M],北京:人民邮电出版社,2017 
\bibitem{ck4} 周志华,机器学习[M],北京:清华大学出版社,2016
\bibitem{ck5} 才云科技Caicloud, 郑泽宇, 顾思宇,TensorFlow:实战Google深度学习框架[M],北京:电子工业出版社,2017
\bibitem{ck6} 谢梁, 鲁颖, 劳虹岚,Keras快速上手:基于Python的深度学习实战[M]
\bibitem{ck7} (美) Mark Summerfield著 王弘博, 孙传庆译,Python 3程序开发指南[M],北京:人民邮电出版社,2015.02
\bibitem{ck8} (印) Shantanu Tushar, Sarath Lakshman著 门佳译,Linux Shell scripting cookbook[M],北京:人民邮电出版社,2014
\bibitem{ck9} 乐毅, 王斌,深度学习:Caffe之经典模型详解与实战[M],北京:电子工业出版社,2016
\bibitem{ck10}(美) 塞巴斯蒂安·拉施卡著 高明, 徐莹, 陶虎成译,Python机器学习[M],北京:机械工业出版社,2017
\bibitem{ck11}(美) Alexander T. Combs著 黄申译,Python机器学习实践指南[M],北京:人民邮电出版社,2017
\bibitem{ck12}闫俊伢,Python编程基础[M],北京:人民邮电出版社,2016.10
\bibitem{ck13}Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Senior Member, IEEE,SegNet: A Deep Convolutional Encoder-Decoder 
Architecture for Image Segmentation,arxiv:1511.00561v2
\bibitem{ck14}Wei Liu, Dragomir Anguelov, Dumitru Erhan, 
SSD: Single Shot MultiBox Detector,arvix:1512.02325v5
\bibitem{ck15}Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun,
Deep Residual Learning for Image Recognition,arvix:1512.03385v1
\bibitem{ck16}Li Li Houfeng Wang,
Towards Label Imbalance in Multi-label Classification with Many Labels,
arvix:1604:01304v1
\bibitem{ck17}Ivo M. Baltruschat, Hannes Nickisch, Michael Grass,
Comparison of Deep Learning Approaches for
Multi-Label Chest X-Ray Classification,
arvix:1803.02315v1
\bibitem{ck18}Ni Zhuang, Yan Yan, Si Chen,
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute
Classification,
arvix:1805.01282v1
\bibitem{ck19}A Tutorial on Multi-Label Learning,https://www.researchgate.net/publication/270337594
\bibitem{ck20}Antonucci Alessandro, Giorgio Corani , Denis Mauá,
An Ensemble of Bayesian Networks for Multilabel Classification 
\bibitem{ck21}Wei Bi,James T. Kwok,
Efficient Multi-label Classification with Many Labels
\bibitem{ck22}Jonas Gehring,Yajie Miao,Florian Metze,
extracting deep bootleneck features using stacked auto-encoders
\bibitem{ck23}Zhi-Hua Zhou,Min-Ling Zhang,Multi-Instance Multi-Label Learning with
Application to Scene Classification
\bibitem{ck24}Min-Ling Zhang and Zhi-Hua Zhou, Fellow, IEEE,A Review on Multi-Label Learning Algorithms
\bibitem{ck25}Yue Zhu, Kai Ming Ting, and Zhi-Hua Zhou, Fellow, IEEE,
Multi-Label Learning with Emerging New Labels
\bibitem{ck26}宋光慧. 基于迁移学习与深度卷积特征的图像标注方法研究[D].浙江大学,2017.
\bibitem{ck27}刘昊天. 多标记迁移学习算法研究以及在鸟声识别中的应用[D].南京农业大学,2016.
\end{thebibliography}

\end{document}
